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Load Azure Data Lake Storage Data to a Database Using Embulk


Use CData JDBC drivers with the open source ETL/ELT tool Embulk to load Azure Data Lake Storage data to a database.

Embulk is an open source bulk data loader. When paired with the CData JDBC Driver for Azure Data Lake Storage, Embulk easily loads data from Azure Data Lake Storage to any supported destination. In this article, we explain how to use the CData JDBC Driver for Azure Data Lake Storage in Embulk to load Azure Data Lake Storage data to a MySQL dtabase.

With built-in optimized data processing, the CData JDBC Driver offers unmatched performance for interacting with live Azure Data Lake Storage data. When you issue complex SQL queries to Azure Data Lake Storage, the driver pushes supported SQL operations, like filters and aggregations, directly to Azure Data Lake Storage and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Configure a JDBC Connection to Azure Data Lake Storage Data

Before creating a bulk load job in Embulk, note the installation location for the JAR file for the JDBC Driver (typically C:\Program Files\CData\CData JDBC Driver for Azure Data Lake Storage\lib).

Embulk supports JDBC connectivity, so you can easily connect to Azure Data Lake Storage and execute SQL queries. Before creating a bulk load job, create a JDBC URL for authenticating with Azure Data Lake Storage.

Authenticating to a Gen 1 DataLakeStore Account

Gen 1 uses OAuth 2.0 in Azure AD for authentication.

For this, an Active Directory web application is required. You can create one as follows:

  1. Sign in to your Azure Account through the .
  2. Select "Azure Active Directory".
  3. Select "App registrations".
  4. Select "New application registration".
  5. Provide a name and URL for the application. Select Web app for the type of application you want to create.
  6. Select "Required permissions" and change the required permissions for this app. At a minimum, "Azure Data Lake" and "Windows Azure Service Management API" are required.
  7. Select "Key" and generate a new key. Add a description, a duration, and take note of the generated key. You won't be able to see it again.

To authenticate against a Gen 1 DataLakeStore account, the following properties are required:

  • Schema: Set this to ADLSGen1.
  • Account: Set this to the name of the account.
  • OAuthClientId: Set this to the application Id of the app you created.
  • OAuthClientSecret: Set this to the key generated for the app you created.
  • TenantId: Set this to the tenant Id. See the property for more information on how to acquire this.
  • Directory: Set this to the path which will be used to store the replicated file. If not specified, the root directory will be used.

Authenticating to a Gen 2 DataLakeStore Account

To authenticate against a Gen 2 DataLakeStore account, the following properties are required:

  • Schema: Set this to ADLSGen2.
  • Account: Set this to the name of the account.
  • FileSystem: Set this to the file system which will be used for this account.
  • AccessKey: Set this to the access key which will be used to authenticate the calls to the API. See the property for more information on how to acquire this.
  • Directory: Set this to the path which will be used to store the replicated file. If not specified, the root directory will be used.

Built-in Connection String Designer

For assistance in constructing the JDBC URL, use the connection string designer built into the Azure Data Lake Storage JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

java -jar cdata.jdbc.adls.jar

Fill in the connection properties and copy the connection string to the clipboard.

Below is a typical JDBC connection string for Azure Data Lake Storage:

jdbc:adls:Schema=ADLSGen2;Account=myAccount;FileSystem=myFileSystem;AccessKey=myAccessKey;InitiateOAuth=GETANDREFRESH

Load Azure Data Lake Storage Data in Embulk

After installing the CData JDBC Driver and creating a JDBC connection string, install the required Embulk plugins.

Install Embulk Input & Output Plugins

  1. Install the JDBC Input Plugin in Embulk.
    https://github.com/embulk/embulk-input-jdbc/tree/master/embulk-input-jdbc
    2. embulk gem install embulk-input-jdbc
  2. In this article, we use MySQL as the destination database. You can also choose SQL Server, PostgreSQL, or Google BigQuery as the destination using the output Plugins.
    https://github.com/embulk/embulk-output-jdbc/tree/master/embulk-output-mysql embulk gem install embulk-output-mysql

With the input and output plugins installed, we are ready to load Azure Data Lake Storage data into MySQL using Embulk.

Create a Job to Load Azure Data Lake Storage Data

Start by creating a config file in Embulk, using a name like adls-mysql.yml.

  1. For the input plugin options, use the CData JDBC Driver for Azure Data Lake Storage, including the path to the driver JAR file, the driver class (e.g. cdata.jdbc.adls.ADLSDriver), and the JDBC URL from above
  2. For the output plugin options, use the values and credentials for the MySQL database

Sample Config File (adls-mysql.yml)

in: type: jdbc driver_path: C:\Program Files\CData[product_name] 20xx\lib\cdata.jdbc.adls.jar driver_class: cdata.jdbc.adls.ADLSDriver url: jdbc:adls:Schema=ADLSGen2;Account=myAccount;FileSystem=myFileSystem;AccessKey=myAccessKey;InitiateOAuth=REFRESH table: "Resources" out: type: mysql host: localhost database: DatabaseName user: UserId password: UserPassword table: "Resources" mode: insert

After creating the file, run the Embulk job.

embulk run adls-mysql.yml

After running the the Embulk job, find the Salesforce data in the MySQL table.

Load Filtered Azure Data Lake Storage Data

In addition to loading data directly from a table, you can use a custom SQL query to have more granular control of the data loaded. You can also perform increment loads by setting a last updated column in a SQL WHERE clause in the query field.

in: type: jdbc driver_path: C:\Program Files\CData[product_name] 20xx\lib\cdata.jdbc.adls.jar driver_class: cdata.jdbc.adls.ADLSDriver url: jdbc:adls:Schema=ADLSGen2;Account=myAccount;FileSystem=myFileSystem;AccessKey=myAccessKey;InitiateOAuth=REFRESH query: "SELECT FullPath, Permission FROM Resources WHERE [RecordId] = 1" out: type: mysql host: localhost database: DatabaseName user: UserId password: UserPassword table: "Resources" mode: insert

More Information & Free Trial

By using CData JDBC Driver for Azure Data Lake Storage as a connector, Embulk can integrate Azure Data Lake Storage data into your data load jobs. And with drivers for more than 200+ other enterprise sources, you can integrate any enterprise SaaS, big data, or NoSQL source as well. Download a 30-day free trial and get started today.